Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features

Basit Öğe Kaydı
dc.contributor.authorKavdir, I
dc.contributor.authorGuyer, DE
dc.date.accessioned2025-01-27T20:43:46Z
dc.date.available2025-01-27T20:43:46Z
dc.date.issued2004
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractEmpire and Golden Delicious apples were classified based on their surface quality conditions using backpropagation neural networks (BPNN) and statistical classifiers such as decision tree (DT), K nearest neighbour (K-NN) and Bayesian with textural features (and histogram features only with the BPNN classifier) extracted using all the pixels in an entire apple image. Two classification applications were performed: two subsets that included a defective (or stem/calyx) apple group and a good apple group; and five subsets that included all the defective (leaf roller, bruise and puncture on Empire, and bruise bitter pit and russet on Golden Delicious) and good apple groups (good tissue and stem/calyx views). With two subsets, classification accuracy using textural features ranged between 72(.)2 and 100% for Empire apples while it ranged between 76(.)5 and 100% for Golden Delicious apples. Results obtained using histogram features were significantly lower than the other classification applications. With five subsets, slightly lower recognition accuracies were obtained; the BPNN using textural features performed 93(.)8% success rate in recognising Empire apples. However, for Golden Delicious apples, all the classifiers produced similar accuracy rates ranging between 85(.)9 and 89(.)7%. Results obtained from the BPNN using histogram features were significantly lower than the classification applications using textural features. (C) 2004 Silsoe Research Institute. All rights reserved Published by Elsevier Ltd.
dc.identifier.doi10.1016/j.biosystemseng.2004.08.008
dc.identifier.endpage344
dc.identifier.issn1537-5110
dc.identifier.issue3
dc.identifier.scopus2-s2.0-9244251098
dc.identifier.scopusqualityQ1
dc.identifier.startpage331
dc.identifier.urihttps://doi.org/10.1016/j.biosystemseng.2004.08.008
dc.identifier.urihttps://hdl.handle.net/20.500.12428/24365
dc.identifier.volume89
dc.identifier.wosWOS:000225759800008
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcademic Press Inc Elsevier Science
dc.relation.ispartofBiosystems Engineering
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectMachine Vision
dc.subjectDefects
dc.subjectBruises
dc.titleComparison of artificial neural networks and statistical classifiers in apple sorting using textural features
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu